API Reference — peewee 2.8.0 documentation

# API Reference

## Models

class `Model`(**kwargs)

  Models provide a 1-to-1 mapping to database tables. Subclasses of `Model` declare any number of `Field` instances as class attributes. These fields correspond to columns on the table.

  Table-level operations, such as `select()`, `update()`, `insert()`, and `delete()`, are implemented as classmethods. Row-level operations such as `save()` and `delete_instance()` are implemented as instancemethods.

  Parameters: | kwargs – Initialize the model, assigning the given key/values to the appropriate fields.

  Example:

      class User(Model):
          username = CharField()
          join_date = DateTimeField(default=datetime.datetime.now)
          is_admin = BooleanField()

      u = User(username='charlie', is_admin=True)

  classmethod `select`(*selection)

    Parameters: | selection – A list of model classes, field instances, functions or expressions. If no argument is provided, all columns for the given model will be selected.
    Return type: | a `SelectQuery` for the given `Model`.

    Examples of selecting all columns (default):

        User.select().where(User.active == True).order_by(User.username)

    Example of selecting all columns on Tweet and the parent model, User. When the `user` foreign key is accessed on a Tweet instance no additional query will be needed (see N+1 for more details):

        (Tweet
          .select(Tweet, User)
          .join(User)
          .order_by(Tweet.created_date.desc()))

  classmethod `update`(**update)

    Parameters: | update – mapping of field-name to expression
    Return type: | an `UpdateQuery` for the given `Model`

    Example showing users being marked inactive if their registration expired:

        q = User.update(active=False).where(User.registration_expired == True)
        q.execute()  # Execute the query, updating the database.

    Example showing an atomic update:

        q = PageView.update(count=PageView.count + 1).where(PageView.url == url)
        q.execute()  # execute the query, updating the database.

    Note

    When an update query is executed, the number of rows modified will be returned.

  classmethod `insert`(**insert)

    Insert a new row into the database. If any fields on the model have default values, these values will be used if the fields are not explicitly set in the `insert` dictionary.

    Parameters: | insert – mapping of field or field-name to expression.
    Return type: | an `InsertQuery` for the given `Model`.

    Example showing creation of a new user:

        q = User.insert(username='admin', active=True, registration_expired=False)
        q.execute()  # perform the insert.

    You can also use `Field` objects as the keys:

        User.insert(**{User.username: 'admin'}).execute()

    If you have a model with a default value on one of the fields, and that field is not specified in the `insert` parameter, the default will be used:

        class User(Model):
            username = CharField()
            active = BooleanField(default=True)

        # This INSERT query will automatically specify `active=True`:
        User.insert(username='charlie')

    Note

    When an insert query is executed on a table with an auto-incrementing primary key, the primary key of the new row will be returned.

  `insert_many`(rows)

    Insert multiple rows at once. The `rows` parameter must be an iterable that yields dictionaries. As with `insert()`, fields that are not specified in the dictionary will use their default value, if one exists.

    Note

    Due to the nature of bulk inserts, each row must contain the same fields. The following will not work:

        Person.insert_many([
            {'first_name': 'Peewee', 'last_name': 'Herman'},
            {'first_name': 'Huey'},  # Missing "last_name"!
        ])

    Parameters: | rows – An iterable containing dictionaries of field-name-to-value.
    Return type: | an `InsertQuery` for the given `Model`.

    Example of inserting multiple Users:

        usernames = ['charlie', 'huey', 'peewee', 'mickey']
        row_dicts = ({'username': username} for username in usernames)

        # Insert 4 new rows.
        User.insert_many(row_dicts).execute()

    Because the `rows` parameter can be an arbitrary iterable, you can also use a generator:

        def get_usernames():
            for username in ['charlie', 'huey', 'peewee']:
                yield {'username': username}
        User.insert_many(get_usernames()).execute()

  classmethod `insert_from`(fields, query)

    Insert rows into the table using a query as the data source. This API should be used for INSERT INTO...SELECT FROM queries.

    Parameters: | fields – The field objects to map the selected data into. query – The source of the new rows.
    Return type: | an `InsertQuery` for the given `Model`.

    Example of inserting data across tables for denormalization purposes:

        source = (User
                  .select(User.username, fn.COUNT(Tweet.id))
                  .join(Tweet, JOIN.LEFT_OUTER)
                  .group_by(User.username))
        UserTweetDenorm.insert_from(
            [UserTweetDenorm.username, UserTweetDenorm.num_tweets],
            source).execute()

  classmethod `delete`()

    Return type: | a `DeleteQuery` for the given `Model`.

    Example showing the deletion of all inactive users:

        q = User.delete().where(User.active == False)
        q.execute()  # remove the rows

    Warning

    This method performs a delete on the entire table. To delete a single instance, see `Model.delete_instance()`.

  classmethod `raw`(sql, *params)

    Parameters: | sql – a string SQL expression params – any number of parameters to interpolate
    Return type: | a `RawQuery` for the given `Model`

    Example selecting rows from the User table:

        q = User.raw('select id, username from users')
        for user in q:
            print user.id, user.username

    Note

    Generally the use of `raw` is reserved for those cases where you can significantly optimize a select query. It is useful for select queries since it will return instances of the model.

  classmethod `create`(**attributes)

    Parameters: | attributes – key/value pairs of model attributes
    Return type: | a model instance with the provided attributes

    Example showing the creation of a user (a row will be added to the database):

        user = User.create(username='admin', password='test')

    Note

    The create() method is a shorthand for instantiate-then-save.

  classmethod `get`(*args)

    Parameters: | args – a list of query expressions, e.g. `User.username == 'foo'`
    Return type: | `Model` instance or raises `DoesNotExist` exception

    Get a single row from the database that matches the given query. Raises a `<model-class>.DoesNotExist` if no rows are returned:

        user = User.get(User.username == username, User.active == True)

    This method is also exposed via the `SelectQuery`, though it takes no parameters:

        active = User.select().where(User.active == True)
        try:
            user = active.where(
                (User.username == username) &
                (User.active == True)
            ).get()
        except User.DoesNotExist:
            user = None

    Note

    The `get()` method is shorthand for selecting with a limit of 1. It has the added behavior of raising an exception when no matching row is found. If more than one row is found, the first row returned by the database cursor will be used.

  classmethod `get_or_create`([defaults=None[, **kwargs]])

    Parameters: | defaults (dict) – A dictionary of values to set on newly-created model instances. kwargs – Django-style filters specifying which model to get, and what values to apply to new instances.
    Returns: | A 2-tuple containing the model instance and a boolean indicating whether the instance was created.

    This function attempts to retrieve a model instance based on the provided filters. If no matching model can be found, a new model is created using the parameters specified by the filters and any values in the `defaults` dictionary.

    Example without `get_or_create`:

        # Without `get_or_create`, we might write:
        try:
            person = Person.get(
                (Person.first_name == 'John') &
                (Person.last_name == 'Lennon'))
        except Person.DoesNotExist:
            person = Person.create(
                first_name='John',
                last_name='Lennon',
                birthday=datetime.date(1940, 10, 9))

    Equivalent code using `get_or_create`:

        person, created = Person.get_or_create(
            first_name='John',
            last_name='Lennon',
            defaults={'birthday': datetime.date(1940, 10, 9)})

  classmethod `create_or_get`([**kwargs])

    Parameters: | kwargs – Field name to value for attempting to create a new instance.
    Returns: | A 2-tuple containing the model instance and a boolean indicating whether the instance was created.

    This function attempts to create a model instance based on the provided kwargs. If an `IntegrityError` occurs indicating the violation of a constraint, then Peewee will return the model matching the filters.

    Note

    Peewee will not attempt to match all the kwargs when an `IntegrityError` occurs. Rather, only primary key fields or fields that have a unique constraint will be used to retrieve the matching instance.

    Example:

        # This will succeed, there is no user named 'charlie' currently.
        charlie, created = User.create_or_get(username='charlie')

        # This will return the above object, since an IntegrityError occurs
        # when trying to create an object using "charlie's" primary key.
        user2, created = User.create_or_get(username='foo', id=charlie.id)

        assert user2.username == 'charlie'

  classmethod `alias`()

    Return type: | `ModelAlias` instance

    The `alias()` method is used to create self-joins.

    Example:

        Parent = Category.alias()
        sq = (Category
              .select(Category, Parent)
              .join(Parent, on=(Category.parent == Parent.id))
              .where(Parent.name == 'parent category'))

    Note

    When using a `ModelAlias` in a join, you must explicitly specify the join condition.

  classmethod `create_table`([fail_silently=False])

    Parameters: | fail_silently (bool) – If set to `True`, the method will check for the existence of the table before attempting to create.

    Create the table for the given model, along with any constraints and indexes.

    Example:

        database.connect()
        SomeModel.create_table()  # Execute the create table query.

  classmethod `drop_table`([fail_silently=False[, cascade=False]])

    Parameters: | fail_silently (bool) – If set to `True`, the query will check for the existence of the table before attempting to remove. cascade (bool) – Drop table with `CASCADE` option.

    Drop the table for the given model.

  classmethod `table_exists`()

    Return type: | Boolean whether the table for this model exists in the database

  classmethod `sqlall`()

    Returns: | A list of queries required to create the table and indexes.

  `save`([force_insert=False[, only=None]])

    Parameters: | force_insert (bool) – Whether to force execution of an insert only (list) – A list of fields to persist – when supplied, only the given fields will be persisted.

    Save the given instance, creating or updating depending on whether it has a primary key. If `force_insert=True` an INSERT will be issued regardless of whether or not the primary key exists.

    Example showing saving a model instance:

        user = User()
        user.username = 'some-user'  # does not touch the database
        user.save()  # change is persisted to the db

  `delete_instance`([recursive=False[, delete_nullable=False]])

    Parameters: | recursive – Delete this instance and anything that depends on it, optionally updating those that have nullable dependencies delete_nullable – If doing a recursive delete, delete all dependent objects regardless of whether it could be updated to NULL

    Delete the given instance. Any foreign keys set to cascade on delete will be deleted automatically. For more programmatic control, you can call with recursive=True, which will delete any non-nullable related models (those that are nullable will be set to NULL). If you wish to delete all dependencies regardless of whether they are nullable, set `delete_nullable=True`.

    example:

        some_obj.delete_instance()  # it is gone forever

  `dependencies`([search_nullable=False])

    Parameters: | search_nullable (bool) – Search models related via a nullable foreign key
    Return type: | Generator expression yielding queries and foreign key fields

    Generate a list of queries of dependent models. Yields a 2-tuple containing the query and corresponding foreign key field. Useful for searching dependencies of a model, i.e. things that would be orphaned in the event of a delete.

  `dirty_fields`

    Return a list of fields that were manually set.

    Return type: | list

    Note

    If you just want to persist modified fields, you can call `model.save(only=model.dirty_fields)`.

    If you always want to only save a model’s dirty fields, you can use the Meta option `only_save_dirty = True`. Then, any time you call `Model.save()`, by default only the dirty fields will be saved, e.g.

        class Person(Model):
            first_name = CharField()
            last_name = CharField()
            dob = DateField()

            class Meta:
                database = db
                only_save_dirty = True

  `is_dirty`()

    Return whether any fields were manually set.

    Return type: | bool

  `prepared`()

    This method provides a hook for performing model initialization after the row data has been populated.

## Fields

`Field(null=False, index=False, unique=False, verbose_name=None, help_text=None, db_column=None, default=None, choices=None, primary_key=False, sequence=None, constraints=None, schema=None, **kwargs):`

  The base class from which all other field types extend.

  Parameters: | null (bool) – whether this column can accept `None` or `NULL` values index (bool) – whether to create an index for this column when creating the table unique (bool) – whether to create a unique index for this column when creating the table verbose_name (string) – specify a “verbose name” for this field, useful for metadata purposes help_text (string) – specify some instruction text for the usage/meaning of this field db_column (string) – column name to use for underlying storage, useful for compatibility with legacy databases default – a value to use as an uninitialized default choices – an iterable of 2-tuples mapping `value` to `display` primary_key (bool) – whether to use this as the primary key for the table sequence (string) – name of sequence (if backend supports it) constraints (list) – a list of constraints, e.g. `[Check('price > 0')]`. schema (string) – name of schema (if backend supports it) kwargs – named attributes containing values that may pertain to specific field subclasses, such as “max_length” or “decimal_places”

  `db_field = '<some field type>'`

    Attribute used to map this field to a column type, e.g. “string” or “datetime”

  `_is_bound`

    Boolean flag indicating if the field is attached to a model class.

  `model_class`

    The model the field belongs to. Only applies to bound fields.

  `name`

    The name of the field. Only applies to bound fields.

  `db_value`(value)

    Parameters: | value – python data type to prep for storage in the database
    Return type: | converted python datatype

  `python_value`(value)

    Parameters: | value – data coming from the backend storage
    Return type: | python data type

  `coerce`(value)

    This method is a shorthand that is used, by default, by both `db_value` and `python_value`. You can usually get away with just implementing this.

    Parameters: | value – arbitrary data from app or backend
    Return type: | python data type

class `IntegerField`

  Stores: integers

  `db_field = 'int'`

class `BigIntegerField`

  Stores: big integers

  `db_field = 'bigint'`

class `PrimaryKeyField`

  Stores: auto-incrementing integer fields suitable for use as primary key.

  `db_field = 'primary_key'`

class `FloatField`

  Stores: floating-point numbers

  `db_field = 'float'`

class `DoubleField`

  Stores: double-precision floating-point numbers

  `db_field = 'double'`

class `DecimalField`

  Stores: decimal numbers, using python standard library `Decimal` objects

  Additional attributes and values:

  `max_digits` | `10`
  `decimal_places` | `5`
  `auto_round` | `False`
  `rounding` | `decimal.DefaultContext.rounding`

  `db_field = 'decimal'`

class `CharField`

  Stores: small strings (0-255 bytes)

  Additional attributes and values:

  `max_length` | `255`

  `db_field = 'string'`

class `TextField`

  Stores: arbitrarily large strings

  `db_field = 'text'`

class `DateTimeField`

  Stores: python `datetime.datetime` instances

  Accepts a special parameter `formats`, which contains a list of formats the datetime can be encoded with. The default behavior is:

      '%Y-%m-%d %H:%M:%S.%f' # year-month-day hour-minute-second.microsecond
      '%Y-%m-%d %H:%M:%S' # year-month-day hour-minute-second
      '%Y-%m-%d' # year-month-day

  Note

  If the incoming value does not match a format, it will be returned as-is

  `db_field = 'datetime'`
  `year`

    An expression suitable for extracting the year, for example to retrieve all blog posts from 2013:

        Blog.select().where(Blog.pub_date.year == 2013)

  `month`

    An expression suitable for extracting the month from a stored date.

  `day`

    An expression suitable for extracting the day from a stored date.

  `hour`

    An expression suitable for extracting the hour from a stored time.

  `minute`

    An expression suitable for extracting the minute from a stored time.

  `second`

    An expression suitable for extracting the second from a stored time.

class `DateField`

  Stores: python `datetime.date` instances

  Accepts a special parameter `formats`, which contains a list of formats the date can be encoded with. The default behavior is:

      '%Y-%m-%d' # year-month-day
      '%Y-%m-%d %H:%M:%S' # year-month-day hour-minute-second
      '%Y-%m-%d %H:%M:%S.%f' # year-month-day hour-minute-second.microsecond

  Note

  If the incoming value does not match a format, it will be returned as-is

  `db_field = 'date'`
  `year`

    An expression suitable for extracting the year, for example to retrieve all people born in 1980:

        Person.select().where(Person.dob.year == 1983)

  `month`

    Same as `year`, except extract month.

  `day`

    Same as `year`, except extract day.

class `TimeField`

  Stores: python `datetime.time` instances

  Accepts a special parameter `formats`, which contains a list of formats the time can be encoded with. The default behavior is:

      '%H:%M:%S.%f' # hour:minute:second.microsecond
      '%H:%M:%S' # hour:minute:second
      '%H:%M' # hour:minute
      '%Y-%m-%d %H:%M:%S.%f' # year-month-day hour-minute-second.microsecond
      '%Y-%m-%d %H:%M:%S' # year-month-day hour-minute-second

  Note

  If the incoming value does not match a format, it will be returned as-is

  `db_field = 'time'`
  `hour`

    Extract the hour from a time, for example to retreive all events occurring in the evening:

        Event.select().where(Event.time.hour > 17)

  `minute`

    Same as `hour`, except extract minute.

  `second`

    Same as `hour`, except extract second..

class `BooleanField`

  Stores: `True` / `False`

  `db_field = 'bool'`

class `BlobField`

  Store arbitrary binary data.

class `UUIDField`

  Store `UUID` values.

  Note

  Currently this field is only supported by `PostgresqlDatabase`.

class `BareField`

  Intended to be used only with SQLite. Since data-types are not enforced, you can declare fields without any data-type. It is also common for SQLite virtual tables to use meta-columns or untyped columns, so for those cases as well you may wish to use an untyped field.

  Accepts a special `coerce` parameter, a function that takes a value coming from the database and converts it into the appropriate Python type.

  Note

  Currently this field is only supported by `SqliteDatabase`.

class `ForeignKeyField`(rel_model[, related_name=None[, on_delete=None[, on_update=None[, to_field=None[, ...]]]]])

  Stores: relationship to another model

  Parameters: | rel_model – related `Model` class or the string ‘self’ if declaring a self-referential foreign key related_name (string) – attribute to expose on related model on_delete (string) – on delete behavior, e.g. `on_delete='CASCADE'`. on_update (string) – on update behavior. to_field – the field (or field name) on `rel_model` the foreign key references. Defaults to the primary key field for `rel_model`.

      class User(Model):
          name = CharField()

      class Tweet(Model):
          user = ForeignKeyField(User, related_name='tweets')
          content = TextField()

      # "user" attribute
      >>> some_tweet.user
      <User: charlie>

      # "tweets" related name attribute
      >>> for tweet in charlie.tweets:
      ...     print tweet.content
      Some tweet
      Another tweet
      Yet another tweet

  Note

  Foreign keys do not have a particular `db_field` as they will take their field type depending on the type of primary key on the model they are related to.

  Note

  If you manually specify a `to_field`, that field must be either a primary key or have a unique constraint.

class `CompositeKey`(*fields)

  Specify a composite primary key for a model. Unlike the other fields, a composite key is defined in the model’s `Meta` class after the fields have been defined. It takes as parameters the string names of the fields to use as the primary key:

      class BlogTagThrough(Model):
          blog = ForeignKeyField(Blog, related_name='tags')
          tag = ForeignKeyField(Tag, related_name='blogs')

          class Meta:
              primary_key = CompositeKey('blog', 'tag')

## Query Types

class `Query`

  The parent class from which all other query classes are drived. While you will not deal with `Query` directly in your code, it implements some methods that are common across all query types.

  `where`(*expressions)

    Parameters: | expressions – a list of one or more expressions
    Return type: | a `Query` instance

    Example selection users where the username is equal to ‘somebody’:

        sq = SelectQuery(User).where(User.username == 'somebody')

    Example selecting tweets made by users who are either editors or administrators:

        sq = SelectQuery(Tweet).join(User).where(
            (User.is_editor == True) |
            (User.is_admin == True))

    Example of deleting tweets by users who are no longer active:

        dq = DeleteQuery(Tweet).where(
            Tweet.user << User.select().where(User.active == False))
        dq.execute()  # perform the delete query

    Note

    `where()` calls are chainable. Multiple calls will be “AND”-ed together.

  `join`(model, join_type=None, on=None)

    Parameters: | model – the model to join on. there must be a `ForeignKeyField` between the current `query context` and the model passed in. join_type – allows the type of `JOIN` used to be specified explicitly, one of `JOIN.INNER`, `JOIN.LEFT_OUTER`, `JOIN.FULL` on – if multiple foreign keys exist between two models, this parameter is the ForeignKeyField to join on.
    Return type: | a `Query` instance

    Generate a `JOIN` clause from the current `query context` to the `model` passed in, and establishes `model` as the new `query context`.

    Example selecting tweets and joining on user in order to restrict to only those tweets made by “admin” users:

        sq = SelectQuery(Tweet).join(User).where(User.is_admin == True)

    Example selecting users and joining on a particular foreign key field. See the example app for a real-life usage:

        sq = SelectQuery(User).join(Relationship, on=Relationship.to_user)

  `switch`(model)

    Parameters: | model – model to switch the `query context` to.
    Return type: | a clone of the query with a new query context

    Switches the `query context` to the given model. Raises an exception if the model has not been selected or joined on previously. Useful for performing multiple joins from a single table.

    The following example selects from blog and joins on both entry and user:

        sq = SelectQuery(Blog).join(Entry).switch(Blog).join(User)

  `alias`(alias=None)

    Parameters: | alias (str) – A string to alias the result of this query
    Return type: | a Query instance

    Assign an alias to given query, which can be used as part of a subquery.

  `sql`()

    Return type: | a 2-tuple containing the appropriate SQL query and a tuple of parameters

  `execute`()

    Execute the given query

  `scalar`([as_tuple=False])

    Parameters: | as_tuple (bool) – return the row as a tuple or a single value
    Return type: | the resulting row, either as a single value or tuple

    Provide a way to retrieve single values from select queries, for instance when performing an aggregation.

        >>> PageView.select(fn.Count(fn.Distinct(PageView.url))).scalar()
        100 # <-- there are 100 distinct URLs in the pageview table

class `SelectQuery`(model_class, *selection)

  By far the most complex of the query classes available in peewee. It supports all clauses commonly associated with select queries.

  Methods on the select query can be chained together.

  `SelectQuery` implements an `__iter__()` method, allowing it to be iterated to return model instances.

  Parameters: | model – a `Model` class to perform query on selection – a list of models, fields, functions or expressions

  If no selection is provided, it will default to all the fields of the given model.

  Example selecting some user instances from the database. Only the `id` and `username` columns are selected. When iterated, will return instances of the `User` model:

      sq = SelectQuery(User, User.id, User.username)
      for user in sq:
          print user.username

  Example selecting users and additionally the number of tweets made by the user. The `User` instances returned will have an additional attribute, ‘count’, that corresponds to the number of tweets made:

      sq = (SelectQuery(
          User, User, fn.Count(Tweet.id).alias('count'))
          .join(Tweet)
          .group_by(User))

  `select`(*selection)

    Parameters: | selection – a list of expressions, which can be model classes or fields. if left blank, will default to all the fields of the given model.
    Return type: | `SelectQuery`

    Note

    Usually the selection will be specified when the instance is created. This method simply exists for the case when you want to modify the SELECT clause independent of instantiating a query.

        query = User.select()
        query = query.select(User.username)

  `from_`(*args)

    Parameters: | args – one or more expressions, for example `Model` or `SelectQuery` instance(s). if left blank, will default to the table of the given model.
    Return type: | `SelectQuery`

        # rather than a join, select from both tables and join with where.
        query = User.select().from_(User, Blog).where(Blog.user == User.id)

  `group_by`(*clauses)

    Parameters: | clauses – a list of expressions, which can be model classes or individual field instances
    Return type: | `SelectQuery`

    Group by one or more columns. If a model class is provided, all the fields on that model class will be used.

    Example selecting users, joining on tweets, and grouping by the user so a count of tweets can be calculated for each user:

        sq = (User
            .select(User, fn.Count(Tweet.id).alias('count'))
            .join(Tweet)
            .group_by(User))

  `having`(*expressions)

    Parameters: | expressions – a list of one or more expressions
    Return type: | `SelectQuery`

    Here is the above example selecting users and tweet counts, but restricting the results to those users who have created 100 or more tweets:

        sq = (User
            .select(User, fn.Count(Tweet.id).alias('count'))
            .join(Tweet)
            .group_by(User)
            .having(fn.Count(Tweet.id) > 100))

  `order_by`(*clauses)

    Parameters: | clauses – a list of fields, calls to `field.[asc|desc]()` or one or more expressions
    Return type: | `SelectQuery`

    Example of ordering users by username:

        User.select().order_by(User.username)

    Example of selecting tweets and ordering them first by user, then newest first:

        query = (Tweet
                 .select()
                 .join(User)
                 .order_by(
                     User.username,
                     Tweet.created_date.desc()))

    You can also use `+` and `-` prefixes to indicate ascending or descending order if you prefer:

        query = (Tweet
                 .select()
                 .join(User)
                 .order_by(
                     +User.username,
                     -Tweet.created_date))

    A more complex example ordering users by the number of tweets made (greatest to least), then ordered by username in the event of a tie:

        tweet_ct = fn.Count(Tweet.id)
        sq = (User
            .select(User, tweet_ct.alias('count'))
            .join(Tweet)
            .group_by(User)
            .order_by(tweet_ct.desc(), User.username))

  `window`(*windows)

    Parameters: | windows (Window) – One or more `Window` instances.

    Add one or more window definitions to this query.

        window = Window(partition_by=[fn.date_trunc('day', PageView.timestamp)])
        query = (PageView
                 .select(
                     PageView.url,
                     PageView.timestamp,
                     fn.Count(PageView.id).over(window=window))
                 .window(window)
                 .order_by(PageView.timestamp))

  `limit`(num)

    Parameters: | num (int) – limit results to `num` rows

  `offset`(num)

    Parameters: | num (int) – offset results by `num` rows

  `paginate`(page_num, paginate_by=20)

    Parameters: | page_num – a 1-based page number to use for paginating results paginate_by – number of results to return per-page
    Return type: | `SelectQuery`

    Shorthand for applying a `LIMIT` and `OFFSET` to the query.

    Page indices are 1-based, so page 1 is the first page.

        User.select().order_by(User.username).paginate(3, 20)  # get users 41-60

  `distinct`([is_distinct=True])

    Parameters: | is_distinct – See notes.
    Return type: | `SelectQuery`

    Indicates that this query should only return distinct rows. Results in a `SELECT DISTINCT` query.

    Note

    The value for `is_distinct` should either be a boolean, in which case the query will (or won’t) be DISTINCT.

    You can specify a list of one or more expressions to generate a `DISTINCT ON` query, e.g. `.distinct([Model.col1, Model.col2])`.

  `for_update`([for_update=True[, nowait=False]])

    Return type: | `SelectQuery`

    Indicate that this query should lock rows for update. If `nowait` is `True` then the database will raise an `OperationalError` if it cannot obtain the lock.

  `naive`()

    Return type: | `SelectQuery`

    Flag this query indicating it should only attempt to reconstruct a single model instance for every row returned by the cursor. If multiple tables were queried, the columns returned are patched directly onto the single model instance.

    Generally this method is useful for speeding up the time needed to construct model instances given a database cursor.

    Note

    this can provide a significant speed improvement when doing simple iteration over a large result set.

  `iterator`()

    Return type: | `iterable`

    By default peewee will cache rows returned by the cursor. This is to prevent things like multiple iterations, slicing and indexing from triggering extra queries. When you are iterating over a large number of rows, however, this cache can take up a lot of memory. Using `iterator()` will save memory by not storing all the returned model instances.

        # iterate over large number of rows.
        for obj in Stats.select().iterator():
            # do something.
            pass

  `tuples`()

    Return type: | `SelectQuery`

    Flag this query indicating it should simply return raw tuples from the cursor. This method is useful when you either do not want or do not need full model instances.

  `dicts`()

    Return type: | `SelectQuery`

    Flag this query indicating it should simply return dictionaries from the cursor. This method is useful when you either do not want or do not need full model instances.

  `aggregate_rows`()

    Return type: | `SelectQuery`

    This method provides one way to avoid the N+1 query problem.

    Consider a webpage where you wish to display a list of users and all of their associated tweets. You could approach this problem by listing the users, then for each user executing a separate query to retrieve their tweets. This is the N+1 behavior, because the number of queries varies depending on the number of users. Conventional wisdom is that it is preferable to execute fewer queries. Peewee provides several ways to avoid this problem.

    You can use the `prefetch()` helper, which uses `IN` clauses to retrieve the tweets for the listed users.

    Another method is to select both the user and the tweet data in a single query, then de-dupe the users, aggregating the tweets in the process.

    The raw column data might appear like this:

        # user.id, user.username, tweet.id, tweet.user_id, tweet.message
        [1,        'charlie',     1,        1,             'hello'],
        [1,        'charlie',     2,        1,             'goodbye'],
        [2,        'no-tweets',   NULL,     NULL,          NULL],
        [3,        'huey',        3,        3,             'meow'],
        [3,        'huey',        4,        3,             'purr'],
        [3,        'huey',        5,        3,             'hiss'],

    We can infer from the `JOIN` clause that the user data will be duplicated, and therefore by de-duping the users, we can collect their tweets in one go and iterate over the users and tweets transparently.

        query = (User
                 .select(User, Tweet)
                 .join(Tweet, JOIN.LEFT_OUTER)
                 .order_by(User.username, Tweet.id)
                 .aggregate_rows())  # .aggregate_rows() tells peewee to de-dupe the rows.
        for user in query:
            print user.username
            for tweet in user.tweets:
                print '  ', tweet.message

        # Producing the following output:
        charlie
           hello
           goodbye
        huey
           meow
           purr
           hiss
        no-tweets

    Warning

    Be sure that you specify an `ORDER BY` clause that ensures duplicated data will appear in consecutive rows.

    Note

    You can specify arbitrarily complex joins, though for more complex queries it may be more efficient to use `prefetch()`. In short, try both and see what works best for your data-set.

    Note

    For more information, see the Avoiding N+1 queries document and the Using aggregate_rows sub-section.

  `annotate`(related_model, aggregation=None)

    Parameters: | related_model – related `Model` on which to perform aggregation, must be linked by `ForeignKeyField`. aggregation – the type of aggregation to use, e.g. `fn.Count(Tweet.id).alias('count')`
    Return type: | `SelectQuery`

    Annotate a query with an aggregation performed on a related model, for example, “get a list of users with the number of tweets for each”:

        >>> User.select().annotate(Tweet)

    If `aggregation` is None, it will default to `fn.Count(related_model.id).alias('count')` but can be anything:

        >>> user_latest = User.select().annotate(Tweet, fn.Max(Tweet.created_date).alias('latest'))

    Note

    If the `ForeignKeyField` is `nullable`, then a `LEFT OUTER` join may need to be used:

        query = (User
                 .select()
                 .join(Tweet, JOIN.LEFT_OUTER)
                 .switch(User)  # Switch query context back to `User`.
                 .annotate(Tweet))

  `aggregate`(aggregation)

    Parameters: | aggregation – a function specifying what aggregation to perform, for example `fn.Max(Tweet.created_date)`.

    Method to look at an aggregate of rows using a given function and return a scalar value, such as the count of all rows or the average value of a particular column.

  `count`([clear_limit=False])

    Parameters: | clear_limit (bool) – Remove any limit or offset clauses from the query before counting.
    Return type: | an integer representing the number of rows in the current query

    Note

    If the query has a GROUP BY, DISTINCT, LIMIT, or OFFSET clause, then the `wrapped_count()` method will be used instead.

        >>> sq = SelectQuery(Tweet)
        >>> sq.count()
        45  # number of tweets
        >>> deleted_tweets = sq.where(Tweet.status == DELETED)
        >>> deleted_tweets.count()
        3  # number of tweets that are marked as deleted

  `wrapped_count`([clear_limit=True])

    Parameters: | clear_limit (bool) – Remove any limit or offset clauses from the query before counting.
    Return type: | an integer representing the number of rows in the current query

    Wrap the count query in a subquery. Additional overhead but will give correct counts when performing `DISTINCT` queries or those with `GROUP BY` clauses.

    Note

    `count()` will automatically default to `wrapped_count()` in the event the query is distinct or has a grouping.

  `exists`()

    Return type: | boolean whether the current query will return any rows. uses an optimized lookup, so use this rather than `get()`.

        sq = User.select().where(User.active == True)
        if sq.where(User.username == username, User.active == True).exists():
            authenticated = True

  `get`()

    Return type: | `Model` instance or raises `DoesNotExist` exception

    Get a single row from the database that matches the given query. Raises a `<model-class>.DoesNotExist` if no rows are returned:

        active = User.select().where(User.active == True)
        try:
            user = active.where(User.username == username).get()
        except User.DoesNotExist:
            user = None

    This method is also exposed via the `Model` api, in which case it accepts arguments that are translated to the where clause:

    > user = User.get(User.active == True, User.username == username)

  `first`()

    Return type: | `Model` instance or `None` if no results

    Fetch the first row from a query. The result will be cached in case the entire query result-set should be iterated later.

  `execute`()

    Return type: | `QueryResultWrapper`

    Executes the query and returns a `QueryResultWrapper` for iterating over the result set. The results are managed internally by the query and whenever a clause is added that would possibly alter the result set, the query is marked for re-execution.

  `__iter__`()

    Executes the query and returns populated model instances:

        for user in User.select().where(User.active == True):
            print user.username

  `__len__`()

    Return the number of items in the result set of this query. If all you need is the count of items and do not intend to do anything with the results, call `count()`.

    Warning

    The `SELECT` query will be executed and the result set will be loaded.

  `__getitem__`(value)

    Parameters: | value – Either an index or a `slice` object.

    Return the model instance(s) at the requested indices. To get the first model, for instance:

        query = User.select().order_by(User.username)
        first_user = query[0]
        first_five = query[:5]

  `__or__`(rhs)

    Parameters: | rhs – Either a `SelectQuery` or a `CompoundSelect`
    Return type: | `CompoundSelect`

    Create a `UNION` query with the right-hand object. The result will contain all values from both the left and right queries.

        customers = Customer.select(Customer.city).where(Customer.state == 'KS')
        stores = Store.select(Store.city).where(Store.state == 'KS')

        # Get all cities in kansas where we have either a customer or a store.
        all_cities = (customers | stores).order_by(SQL('city'))

    Note

    SQLite does not allow `ORDER BY` or `LIMIT` clauses on the components of a compound query, however SQLite does allow these clauses on the final, compound result. This applies to `UNION (ALL)`, `INTERSECT`, and `EXCEPT`.

  `__and__`(rhs)

    Parameters: | rhs – Either a `SelectQuery` or a `CompoundSelect`
    Return type: | `CompoundSelect`

    Create an `INTERSECT` query. The result will contain values that are in both the left and right queries.

        customers = Customer.select(Customer.city).where(Customer.state == 'KS')
        stores = Store.select(Store.city).where(Store.state == 'KS')

        # Get all cities in kanasas where we have both customers and stores.
        cities = (customers & stores).order_by(SQL('city'))

  `__sub__`(rhs)

    Parameters: | rhs – Either a `SelectQuery` or a `CompoundSelect`
    Return type: | `CompoundSelect`

    Create an `EXCEPT` query. The result will contain values that are in the left-hand query but not in the right-hand query.

        customers = Customer.select(Customer.city).where(Customer.state == 'KS')
        stores = Store.select(Store.city).where(Store.state == 'KS')

        # Get all cities in kanasas where we have customers but no stores.
        cities = (customers - stores).order_by(SQL('city'))

  `__xor__`(rhs)

    Parameters: | rhs – Either a `SelectQuery` or a `CompoundSelect`
    Return type: | `CompoundSelect`

    Create an symmetric difference query. The result will contain values that are in either the left-hand query or the right-hand query, but not both.

        customers = Customer.select(Customer.city).where(Customer.state == 'KS')
        stores = Store.select(Store.city).where(Store.state == 'KS')

        # Get all cities in kanasas where we have either customers with no
        # store, or a store with no customers.
        cities = (customers ^ stores).order_by(SQL('city'))

class `UpdateQuery`(model_class, **kwargs)

  Parameters: | model – `Model` class on which to perform update kwargs – mapping of field/value pairs containing columns and values to update

  Example in which users are marked inactive if their registration expired:

      uq = UpdateQuery(User, active=False).where(User.registration_expired == True)
      uq.execute()  # Perform the actual update

  Example of an atomic update:

      atomic_update = UpdateQuery(PageCount, count = PageCount.count + 1).where(
          PageCount.url == url)
      atomic_update.execute()  # will perform the actual update

  `execute`()

    Return type: | Number of rows updated

    Performs the query

  `returning`(*returning)

    Parameters: | returning – A list of model classes, field instances, functions or expressions. If no argument is provided, all columns for the given model will be selected. To clear any existing values, pass in `None`.
    Return type: | a `UpdateQuery` for the given `Model`.

    Add a `RETURNING` clause to the query, which will cause the `UPDATE` to compute return values based on each row that was actually updated.

    When the query is executed, rather than returning the number of rows updated, an iterator will be returned that yields the updated objects.

    Note

    Currently only `PostgresqlDatabase` supports this feature.

    Example:

        # Disable all users whose registration expired, and return the user
        # objects that were updated.
        query = (User
                 .update(active=False)
                 .where(User.registration_expired == True)
                 .returning(User))

        # We can iterate over the users that were updated.
        for updated_user in query.execute():
            send_activation_email(updated_user.email)

    For more information, check out the RETURNING clause docs.

  `tuples`()

    Return type: | `UpdateQuery`

    Note

    This method should only be used in conjunction with a call to `returning()`.

    When the updated results are returned, they will be returned as row tuples.

  `dicts`()

    Return type: | `UpdateQuery`

    Note

    This method should only be used in conjunction with a call to `returning()`.

    When the updated results are returned, they will be returned as dictionaries mapping column to value.

  `on_conflict`([action=None])

    Add a SQL `ON CONFLICT` clause with the specified action to the given `UPDATE` query. Valid actions are:

    - ROLLBACK
    - ABORT
    - FAIL
    - IGNORE
    - REPLACE

    Specifying `None` for the action will execute a normal `UPDATE` query.

    Note

    This feature is only available on SQLite databases.

class `InsertQuery`(model_class[, field_dict=None[, rows=None[, fields=None[, query=None[, validate_fields=False]]]]])

  Creates an `InsertQuery` instance for the given model.

  Parameters: | field_dict (dict) – A mapping of either field or field-name to value. rows (iterable) – An iterable of dictionaries containing a mapping of field or field-name to value. fields (list) – A list of field objects to insert data into (only used in combination with the `query` parameter). query – A `SelectQuery` to use as the source of data. validate_fields (bool) – Check that every column referenced in the insert query has a corresponding field on the model. If validation is enabled and then fails, a `KeyError` is raised.

  Basic example:

      >>> fields = {'username': 'admin', 'password': 'test', 'active': True}
      >>> iq = InsertQuery(User, fields)
      >>> iq.execute()  # insert new row and return primary key
      2L

  Example inserting multiple rows:

      users = [
          {'username': 'charlie', 'active': True},
          {'username': 'peewee', 'active': False},
          {'username': 'huey', 'active': True}]
      iq = InsertQuery(User, rows=users)
      iq.execute()

  Example inserting using a query as the data source:

      query = (User
               .select(User.username, fn.COUNT(Tweet.id))
               .join(Tweet, JOIN.LEFT_OUTER)
               .group_by(User.username))
      iq = InsertQuery(
          UserTweetDenorm,
          fields=[UserTweetDenorm.username, UserTweetDenorm.num_tweets],
          query=query)
      iq.execute()

  `execute`()

    Return type: | primary key of the new row

    Performs the query

  `upsert`([upsert=True])

    Perform an INSERT OR REPLACE query with SQLite. MySQL databases will issue a REPLACE query. Currently this feature is not supported for Postgres databases, but the 9.5 syntax will be added soon.

    Note

    This feature is only available on SQLite and MySQL databases.

  `on_conflict`([action=None])

    Add a SQL `ON CONFLICT` clause with the specified action to the given `INSERT` query. Specifying `REPLACE` is equivalent to using the `upsert()` method. Valid actions are:

    - ROLLBACK
    - ABORT
    - FAIL
    - IGNORE
    - REPLACE

    Specifying `None` for the action will execute a normal `INSERT` query.

    Note

    This feature is only available on SQLite databases.

  `return_id_list`([return_id_list=True])

    By default, when doing bulk INSERTs, peewee will not return the list of generated primary keys. However, if the database supports returning primary keys via `INSERT ... RETURNING`, this method instructs peewee to return the generated list of IDs.

    Note

    Currently only PostgreSQL supports this behavior. While other databases support bulk inserts, they will simply return `True` instead.

    Example:

        usernames = [
            {'username': username}
            for username in ['charlie', 'huey', 'mickey']]
        query = User.insert_many(usernames).return_id_list()
        user_ids = query.execute()
        print user_ids
        # prints something like [1, 2, 3]

  `returning`(*returning)

    Parameters: | returning – A list of model classes, field instances, functions or expressions. If no argument is provided, all columns for the given model will be selected. To clear any existing values, pass in `None`.
    Return type: | a `InsertQuery` for the given `Model`.

    Add a `RETURNING` clause to the query, which will cause the `INSERT` to compute return values based on each row that was inserted.

    When the query is executed, rather than returning the primary key of the new row(s), an iterator will be returned that yields the inserted objects.

    Note

    Currently only `PostgresqlDatabase` supports this feature.

    Example:

        # Create some users, retrieving the list of IDs assigned to them.
        query = (User
                 .insert_many(list_of_user_data)
                 .returning(User))

        # We can iterate over the users that were created.
        for new_user in query.execute():
            # Do something with the new user's ID...
            do_something(new_user.id)

    For more information, check out the RETURNING clause docs.

  `tuples`()

    Return type: | `InsertQuery`

    Note

    This method should only be used in conjunction with a call to `returning()`.

    When the inserted results are returned, they will be returned as row tuples.

  `dicts`()

    Return type: | `InsertQuery`

    Note

    This method should only be used in conjunction with a call to `returning()`.

    When the inserted results are returned, they will be returned as dictionaries mapping column to value.

class `DeleteQuery`(model_class)

  Creates a DELETE query for the given model.

  Note

  DeleteQuery will not traverse foreign keys or ensure that constraints are obeyed, so use it with care.

  Example deleting users whose account is inactive:

      dq = DeleteQuery(User).where(User.active == False)

  `execute`()

    Return type: | Number of rows deleted

    Performs the query

  `returning`(*returning)

    Parameters: | returning – A list of model classes, field instances, functions or expressions. If no argument is provided, all columns for the given model will be selected. To clear any existing values, pass in `None`.
    Return type: | a `DeleteQuery` for the given `Model`.

    Add a `RETURNING` clause to the query, which will cause the `DELETE` to compute return values based on each row that was removed from the database.

    When the query is executed, rather than returning the number of rows deleted, an iterator will be returned that yields the deleted objects.

    Note

    Currently only `PostgresqlDatabase` supports this feature.

    Example:

        # Create some users, retrieving the list of IDs assigned to them.
        query = (User
                 .delete()
                 .where(User.account_expired == True)
                 .returning(User))

        # We can iterate over the user objects that were deleted.
        for deleted_user in query.execute():
            # Do something with the deleted user.
            notify_account_deleted(deleted_user.email)

    For more information, check out the RETURNING clause docs.

  `tuples`()

    Return type: | `DeleteQuery`

    Note

    This method should only be used in conjunction with a call to `returning()`.

    When the deleted results are returned, they will be returned as row tuples.

  `dicts`()

    Return type: | `DeleteQuery`

    Note

    This method should only be used in conjunction with a call to `returning()`.

    When the deleted results are returned, they will be returned as dictionaries mapping column to value.

class `RawQuery`(model_class, sql, *params)

  Allows execution of an arbitrary query and returns instances of the model via a `QueryResultsWrapper`.

  Note

  Generally you will only need this for executing highly optimized SELECT queries.

  Warning

  If you are executing a parameterized query, you must use the correct interpolation string for your database. SQLite uses `'?'` and most others use `'%s'`.

  Example selecting users with a given username:

      >>> rq = RawQuery(User, 'SELECT * FROM users WHERE username = ?', 'admin')
      >>> for obj in rq.execute():
      ...     print obj
      <User: admin>

  `tuples`()

    Return type: | `RawQuery`

    Flag this query indicating it should simply return raw tuples from the cursor. This method is useful when you either do not want or do not need full model instances.

  `dicts`()

    Return type: | `RawQuery`

    Flag this query indicating it should simply return raw dicts from the cursor. This method is useful when you either do not want or do not need full model instances.

  `execute`()

    Return type: | a `QueryResultWrapper` for iterating over the result set. The results are instances of the given model.

    Performs the query

class `CompoundSelect`(model_class, lhs, operator, rhs)

  Compound select query.

  Parameters: | model_class – The type of model to return, by default the model class of the `lhs` query. lhs – Left-hand query, either a `SelectQuery` or a `CompoundQuery`. operator – A `Node` instance used to join the two queries, for example `SQL('UNION')`. rhs – Right query, either a `SelectQuery` or a `CompoundQuery`.

`prefetch`(sq, *subqueries)

  Parameters: | sq – `SelectQuery` instance subqueries – one or more `SelectQuery` instances to prefetch for `sq`. You can also pass models, but they will be converted into SelectQueries. If you wish to specify a particular model to join against, you can pass a 2-tuple of `(query_or_model, join_model)`.
  Return type: | `SelectQuery` with related instances pre-populated

  Pre-fetch the appropriate instances from the subqueries and apply them to their corresponding parent row in the outer query. This function will eagerly load the related instances specified in the subqueries. This is a technique used to save doing O(n) queries for n rows, and rather is O(k) queries for k subqueries.

  For example, consider you have a list of users and want to display all their tweets:

      # let's impost some small restrictions on our queries
      users = User.select().where(User.active == True)
      tweets = Tweet.select().where(Tweet.published == True)

      # this will perform 2 queries
      users_pf = prefetch(users, tweets)

      # now we can:
      for user in users_pf:
          print user.username
          for tweet in user.tweets_prefetch:
              print '- ', tweet.content

  You can prefetch an arbitrary number of items. For instance, suppose we have a photo site, User -> Photo -> (Comments, Tags). That is, users can post photos, and these photos can have tags and comments on them. If we wanted to fetch a list of users, all their photos, and all the comments and tags on the photos:

      users = User.select()
      published_photos = Photo.select().where(Photo.published == True)
      published_comments = Comment.select().where(
          (Comment.is_spam == False) &
          (Comment.num_flags < 3))

      # note that we are just passing the Tag model -- it will be converted
      # to a query automatically
      users_pf = prefetch(users, published_photos, published_comments, Tag)

      # now we can iterate users, photos, and comments/tags
      for user in users_pf:
          for photo in user.photo_set_prefetch:
              for comment in photo.comment_set_prefetch:
                  # ...
              for tag in photo.tag_set_prefetch:
                  # ...

  Note

  Subqueries must be related by foreign key and can be arbitrarily deep

  Note

  For more information, see the Avoiding N+1 queries document and the Using prefetch sub-section.

  Warning

  `prefetch()` can use up lots of RAM when the result set is large, and will not warn you if you are doing something dangerous, so it is up to you to know when to use it. Additionally, because of the semantics of subquerying, there may be some cases when prefetch does not act as you expect (for instance, when applying a `LIMIT` to subqueries, but there may be others) – please report anything you think is a bug to github.

## Database and its subclasses

class `Database`(database[, threadlocals=True[, autocommit=True[, fields=None[, ops=None[, autorollback=False[, use_speedups=True[, **connect_kwargs]]]]]]])

  Parameters: | database – the name of the database (or filename if using sqlite) threadlocals (bool) – whether to store connections in a threadlocal autocommit (bool) – automatically commit every query executed by calling `execute()` fields (dict) – a mapping of `db_field` to database column type, e.g. ‘string’ => ‘varchar’ ops (dict) – a mapping of operations understood by the querycompiler to expressions autorollback (bool) – automatically rollback when an exception occurs while executing a query. use_speedups (bool) – use the Cython speedups module to improve performance of some queries. connect_kwargs – any arbitrary parameters to pass to the database driver when connecting

  The `connect_kwargs` dictionary is used for vendor-specific parameters that will be passed back directly to your database driver, allowing you to specify the `user`, `host` and `password`, for instance. For more information and examples, see the vendor-specific parameters document.

  Note

  If your database name is not known when the class is declared, you can pass `None` in as the database name which will mark the database as “deferred” and any attempt to connect while in this state will raise an exception. To initialize your database, call the `Database.init()` method with the database name.

  For an in-depth discussion of run-time database configuration, see the Run-time database configuration section.

  A high-level API for working with the supported database engines. The database class:

  - Manages the underlying database connection.
  - Executes queries.
  - Manage transactions and savepoints.
  - Create and drop tables and indexes.
  - Introspect the database.
  `commit_select = False`

    Whether to issue a commit after executing a select query. With some engines can prevent implicit transactions from piling up.

  `compiler_class = QueryCompiler`

    A class suitable for compiling queries

  `compound_operations = ['UNION', 'INTERSECT', 'EXCEPT']`

    Supported compound query operations.

  `compound_select_parentheses = False`

    Whether `UNION` (or other compound `SELECT` queries) allow parentheses around the queries.

  `distinct_on = False`

    Whether the database supports `DISTINCT ON` statements.

  `drop_cascade = False`

    Whether the database supports cascading drop table queries.

  `field_overrides = {}`

    A mapping of field types to database column types, e.g. `{'primary_key': 'SERIAL'}`

  `foreign_keys = True`

    Whether the given backend enforces foreign key constraints.

  `for_update = False`

    Whether the given backend supports selecting rows for update

  `for_update_nowait = False`

    Whether the given backend supports selecting rows for update

  `insert_many = True`

    Whether the database supports multiple `VALUES` clauses for `INSERT` queries.

  `insert_returning = False`

    Whether the database supports returning the primary key for newly inserted rows.

  `interpolation = '?'`

    The string used by the driver to interpolate query parameters

  `op_overrides = {}`

    A mapping of operation codes to string operations, e.g. `{OP.LIKE: 'LIKE BINARY'}`

  `quote_char = '"'`

    The string used by the driver to quote names

  `reserved_tables = []`

    Table names that are reserved by the backend – if encountered in the application a warning will be issued.

  `returning_clause = False`

    Whether the database supports `RETURNING` clauses for `UPDATE`, `INSERT` and `DELETE` queries.

    Note

    Currently only `PostgresqlDatabase` supports this.

    See the following for more information:

    - `UpdateQuery.returning()`
    - `InsertQuery.returning()`
    - `DeleteQuery.returning()`
  `savepoints = True`

    Whether the given backend supports savepoints.

  `sequences = False`

    Whether the given backend supports sequences

  `subquery_delete_same_table = True`

    Whether the given backend supports deleting rows using a subquery that selects from the same table

  `window_functions = False`

    Whether the given backend supports window functions.

  `init`(database[, **connect_kwargs])

    This method is used to initialize a deferred database. For details on configuring your database at run-time, see the Run-time database configuration section.

    Parameters: | database – the name of the database (or filename if using sqlite) connect_kwargs – any arbitrary parameters to pass to the database driver when connecting

  `connect`()

    Establishes a connection to the database

    Note

    By default, connections will be stored on a threadlocal, ensuring connections are not shared across threads. To disable this behavior, initialize the database with `threadlocals=False`.

  `close`()

    Closes the connection to the database (if one is open)

    Note

    If you initialized with `threadlocals=True`, only a connection local to the calling thread will be closed.

  `initialize_connection`(conn)

    Perform additional intialization on a newly-opened connection. For example, if you are using SQLite you may want to enable foreign key constraint enforcement (off by default).

    Here is how you might use this hook to load a SQLite extension:

        class CustomSqliteDatabase(SqliteDatabase):
            def initialize_connection(self, conn):
                conn.load_extension('fts5')

  `get_conn`()

    Return type: | a connection to the database, creates one if does not exist

  `get_cursor`()

    Return type: | a cursor for executing queries

  `last_insert_id`(cursor, model)

    Parameters: | cursor – the database cursor used to perform the insert query model – the model class that was just created
    Return type: | the primary key of the most recently inserted instance

  `rows_affected`(cursor)

    Return type: | number of rows affected by the last query

  `compiler`()

    Return type: | an instance of `QueryCompiler` using the field and op overrides specified.

  `execute`(clause)

    Parameters: | clause (Node) – a `Node` instance or subclass (e.g. a `SelectQuery`).

    The clause will be compiled into SQL then sent to the `execute_sql()` method.

  `execute_sql`(sql[, params=None[, require_commit=True]])

    Parameters: | sql – a string sql query params – a list or tuple of parameters to interpolate

    Note

    You can configure whether queries will automatically commit by using the `set_autocommit()` and `Database.get_autocommit()` methods.

  `begin`()

    Initiate a new transaction. By default not implemented as this is not part of the DB-API 2.0, but provided for API compatibility.

  `commit`()

    Call `commit()` on the active connection, committing the current transaction.

  `rollback`()

    Call `rollback()` on the active connection, rolling back the current transaction.

  `set_autocommit`(autocommit)

    Parameters: | autocommit – a boolean value indicating whether to turn on/off autocommit.

  `get_autocommit`()

    Return type: | a boolean value indicating whether autocommit is enabled.

  `get_tables`([schema=None])

    Return type: | a list of table names in the database.

  `get_indexes`(table[, schema=None])

    Return type: | a list of `IndexMetadata` instances, representing the indexes for the given table.

  `get_columns`(table[, schema=None])

    Return type: | a list of `ColumnMetadata` instances, representing the columns for the given table.

  `get_primary_keys`(table[, schema=None])

    Return type: | a list containing the primary key column name(s) for the given table.

  `get_foreign_keys`(table[, schema=None])

    Return type: | a list of `ForeignKeyMetadata` instances, representing the foreign keys for the given table.

  `sequence_exists`(sequence_name)

    Rtype boolean: |

  `create_table`(model_class[, safe=True])

    Parameters: | model_class – `Model` class. safe (bool) – If True, the table will not be created if it already exists.

    Warning

    Unlike `Model.create_table()`, this method does not create indexes or constraints. This method will only create the table itself. If you wish to create the table along with any indexes and constraints, use either `Model.create_table()` or `Database.create_tables()`.

  `create_index`(model_class, fields[, unique=False])

    Parameters: | model_class – `Model` table on which to create index fields – field(s) to create index on (either field instances or field names) unique – whether the index should enforce uniqueness

  `create_foreign_key`(model_class, field[, constraint=None])

    Parameters: | model_class – `Model` table on which to create foreign key constraint field – `Field` object constraint (str) – Name to give foreign key constraint.

    Manually create a foreign key constraint using an `ALTER TABLE` query. This is primarily used when creating a circular foreign key dependency, for example:

        DeferredPost = DeferredRelation()

        class User(Model):
            username = CharField()
            favorite_post = ForeignKeyField(DeferredPost, null=True)

        class Post(Model):
            title = CharField()
            author = ForeignKeyField(User, related_name='posts')

        DeferredPost.set_model(Post)

        # Create tables.  The foreign key from Post -> User will be created
        # automatically, but the foreign key from User -> Post must be added
        # manually.
        User.create_table()
        Post.create_table()

        # Manually add the foreign key constraint on `User`, since we could
        # not add it until we had created the `Post` table.
        db.create_foreign_key(User, User.favorite_post)

  `create_sequence`(sequence_name)

    Parameters: | sequence_name – name of sequence to create

    Note

    only works with database engines that support sequences

  `drop_table`(model_class[, fail_silently=False[, cascade=False]])

    Parameters: | model_class – `Model` table to drop fail_silently (bool) – if `True`, query will add a `IF EXISTS` clause cascade (bool) – drop table with `CASCADE` option.

  `drop_sequence`(sequence_name)

    Parameters: | sequence_name – name of sequence to drop

    Note

    only works with database engines that support sequences

  `create_tables`(models[, safe=False])

    Parameters: | models (list) – A list of models. safe (bool) – Check first whether the table exists before attempting to create it.

    This method should be used for creating tables as it will resolve the model dependency graph and ensure the tables are created in the correct order. This method will also create any indexes and constraints defined on the models.

    Usage:

        db.create_tables([User, Tweet, Something], safe=True)

  `drop_tables`(models[, safe=False[, cascade=False]])

    Parameters: | models (list) – A list of models. safe (bool) – Check the table exists before attempting to drop it. cascade (bool) – drop table with `CASCADE` option.

    This method should be used for dropping tables, as it will resolve the model dependency graph and ensure the tables are dropped in the correct order.

    Usage:

        db.drop_tables([User, Tweet, Something], safe=True)

  `atomic`()

    Execute statements in either a transaction or a savepoint. The outer-most call to atomic will use a transaction, and any subsequent nested calls will use savepoints.

    `atomic` can be used as either a context manager or a decorator.

    Note

    For most use-cases, it makes the most sense to always use `atomic()` when you wish to execute queries in a transaction. The benefit of using `atomic` is that you do not need to manually keep track of the transaction stack depth, as this will be managed for you.

    Context manager example code:

        with db.atomic() as txn:
            perform_some_operations()

            with db.atomic() as nested_txn:
                do_other_things()
                if something_bad_happened():
                    # Roll back these changes, but preserve the changes
                    # made in the outer block.
                    nested_txn.rollback()

    Decorator example code:

        @db.atomic()
        def create_user(username):
            # This function will execute in a transaction/savepoint.
            return User.create(username=username)

  `transaction`()

    Execute statements in a transaction using either a context manager or decorator. If an error is raised inside the wrapped block, the transaction will be rolled back, otherwise statements are committed when exiting. Transactions can also be explicitly rolled back or committed within the transaction block by calling `rollback()` or `commit()`. If you manually commit or roll back, a new transaction will be started automatically.

    Nested blocks can be wrapped with `transaction` - the database will keep a stack and only commit when it reaches the end of the outermost function / block.

    Context manager example code:

        # delete a blog instance and all its associated entries, but
        # do so within a transaction
        with database.transaction():
            blog.delete_instance(recursive=True)

        # Explicitly roll back a transaction.
        with database.transaction() as txn:
            do_some_stuff()
            if something_bad_happened():
                # Roll back any changes made within this block.
                txn.rollback()

    Decorator example code:

        @database.transaction()
        def transfer_money(from_acct, to_acct, amt):
            from_acct.charge(amt)
            to_acct.pay(amt)
            return amt

  `commit_on_success`(func)

    Note

    Use `atomic()` or `transaction()` instead.

  `savepoint`([sid=None])

    Execute statements in a savepoint using either a context manager or decorator. If an error is raised inside the wrapped block, the savepoint will be rolled back, otherwise statements are committed when exiting. Like `transaction()`, a savepoint can also be explicitly rolled-back or committed by calling `rollback()` or `commit()`. If you manually commit or roll back, a new savepoint will not be created.

    Savepoints can be thought of as nested transactions.

    Parameters: | sid (str) – An optional string identifier for the savepoint.

    Context manager example code:

        with db.transaction() as txn:
            do_some_stuff()
            with db.savepoint() as sp1:
                do_more_things()

            with db.savepoint() as sp2:
                even_more()
                # Oops, something bad happened, roll back
                # just the changes made in this block.
                if something_bad_happened():
                    sp2.rollback()

  `execution_context`([with_transaction=True])

    Create an `ExecutionContext` context manager or decorator. Blocks wrapped with an ExecutionContext will run using their own connection. By default, the wrapped block will also run in a transaction, although this can be disabled specifyin `with_transaction=False`.

    For more explanation of `ExecutionContext`, see the Advanced Connection Management section.

    Warning

    ExecutionContext is very new and has not been tested extensively.

  classmethod `register_fields`(fields)

    Register a mapping of field overrides for the database class. Used to register custom fields or override the defaults.

    Parameters: | fields (dict) – A mapping of `db_field` to column type

  classmethod `register_ops`(ops)

    Register a mapping of operations understood by the QueryCompiler to their SQL equivalent, e.g. `{OP.EQ: '='}`. Used to extend the types of field comparisons.

    Parameters: | fields (dict) – A mapping of `db_field` to column type

  `extract_date`(date_part, date_field)

    Return an expression suitable for extracting a date part from a date field. For instance, extract the year from a `DateTimeField`.

    Parameters: | date_part (str) – The date part attribute to retrieve. Valid options are: “year”, “month”, “day”, “hour”, “minute” and “second”. date_field (Field) – field instance storing a datetime, date or time.
    Return type: | an expression object.

  `truncate_date`(date_part, date_field)

    Return an expression suitable for truncating a date / datetime to the given resolution. This can be used, for example, to group a collection of timestamps by day.

    Parameters: | date_part (str) – The date part to truncate to. Valid options are: “year”, “month”, “day”, “hour”, “minute” and “second”. date_field (Field) – field instance storing a datetime, date or time.
    Return type: | an expression object.

    Example:

        # Get tweets from today.
        tweets = Tweet.select().where(
            db.truncate_date('day', Tweet.timestamp) == datetime.date.today())

class `SqliteDatabase`(Database)

  `Database` subclass that works with the `sqlite3` driver (or `pysqlite2`). In addition to the default database parameters, `SqliteDatabase` also accepts a journal_mode parameter which will configure the journaling mode.

  Note

  If you have both `sqlite3` and `pysqlite2` installed on your system, peewee will use whichever points at a newer version of SQLite.

  Note

  SQLite is unique among the databases supported by Peewee in that it allows a high degree of customization by the host application. This means you can do things like write custom functions or aggregates in Python and then call them from your SQL queries. This feature, and many more, are available through the `SqliteExtDatabase`, part of `playhouse.sqlite_ext`. I strongly recommend you use `SqliteExtDatabase` as it exposes many of the features that make SQLite so powerful.

  Custom parameters:

  Parameters: | journal_mode (str) – Journaling mode. pragmas (list) – List of 2-tuples containing `PRAGMA` statements to run against new connections.

  SQLite allows run-time configuration of a number of parameters through `PRAGMA` statements (documentation). These statements are typically run against a new database connection. To run one or more `PRAGMA` statements against new connections, you can specify them as a list of 2-tuples containing the pragma name and value:

      db = SqliteDatabase('my_app.db', pragmas=(
          ('journal_mode', 'WAL'),
          ('cache_size', 10000),
          ('mmap_size', 1024 * 1024 * 32),
      ))

  `insert_many = True *if* using SQLite 3.7.11.0 or newer.`

class `MySQLDatabase`(Database)

  `Database` subclass that works with either “MySQLdb” or “pymysql”.

  `commit_select = True`
  `compound_operations = ['UNION']`
  `for_update = True`
  `subquery_delete_same_table = False`

class `PostgresqlDatabase`(Database)

  `Database` subclass that works with the “psycopg2” driver

  `commit_select = True`
  `compound_select_parentheses = True`
  `distinct_on = True`
  `for_update = True`
  `for_update_nowait = True`
  `insert_returning = True`
  `returning_clause = True`
  `sequences = True`
  `window_functions = True`
  `register_unicode = True`

    Control whether the `UNICODE` and `UNICODEARRAY` psycopg2 extensions are loaded automatically.

## Transaction, Savepoint and ExecutionContext

The easiest way to create transactions and savepoints is to use `Database.atomic()`. The `atomic()` method will create a transaction or savepoint depending on the level of nesting.

    with db.atomic() as txn:
        # The outer-most call will be a transaction.
        with db.atomic() as sp:
            # Nested calls will be savepoints instead.
            execute_some_statements()

class `transaction`(database)

  Context manager that encapsulates a database transaction. Statements executed within the wrapped block will be committed at the end of the block unless an exception occurs, in which case any changes will be rolled back.

  Warning

  Transactions should not be nested as this could lead to unpredictable behavior in the event of an exception in a nested block. If you wish to use nested transactions, use the `atomic()` method, which will create a transaction at the outer-most layer and use savepoints for nested blocks.

  Note

  In practice you should not create `transaction` objects directly, but rather use the `Database.transaction()` method.

  `commit`()

    Manually commit any pending changes and begin a new transaction.

  `rollback`()

    Manually roll-back any pending changes and begin a new transaction.

class `savepoint`(database[, sid=None])

  Context manager that encapsulates a savepoint (nested transaction). Statements executed within the wrapped block will be committed at the end of the block unless an exception occurs, in which case any changes will be rolled back.

  Warning

  Savepoints must be created within a transaction. It is recommended that you use `atomic()` instead of manually managing the transaction+savepoint stack.

  Note

  In practice you should not create `savepoint` objects directly, but rather use the `Database.savepoint()` method.

  `commit`()

    Manually commit any pending changes. If the savepoint is manually committed and additional changes are made, they will be executed in the context of the outer block.

  `rollback`()

    Manually roll-back any pending changes. If the savepoint is manually rolled-back and additional changes are made, they will be executed in the context of the outer block.

class `ExecutionContext`(database[, with_transaction=True])

  ExecutionContext provides a way to explicitly run statements in a dedicated connection. Typically a single database connection is maintained per-thread, but in some situations you may wish to explicitly force a new, separate connection. To accomplish this, you can create an `ExecutionContext`. Statements executed in the wrapped block will be run in a transaction by default, though you can disable this by specifying `with_transaction=False`.

  Note

  Rather than instantiating `ExecutionContext` directly, use `Database.execution_context()`.

  Example code:

      # This will return the connection associated with the current thread.
      conn = db.get_conn()

      with db.execution_context():
          # This will be a new connection object. If you are using the
          # connection pool, it may be an unused connection from the pool.
          ctx_conn = db.get_conn()

          # This statement is executed using the new `ctx_conn`.
          User.create(username='huey')

      # At the end of the wrapped block, the connection will be closed and the
      # transaction, if one exists, will be committed.

      # This statement is executed using the regular `conn`.
      User.create(username='mickey')

class `Using`(database, models[, with_transaction=True])

  For the duration of the wrapped block, all queries against the given `models` will use the specified `database`. Optionally these queries can be run inside a transaction by specifying `with_transaction=True`.

  `Using` provides, in short, a way to run queries on a list of models using a manually specified database.

  Parameters: | database – a `Database` instance. models – a list of `Model` classes to use with the given database. with_transaction – Whether the wrapped block should be run in a transaction.

## Metadata Types

class `IndexMetadata`(name, sql, columns, unique, table)
  `name`

    The name of the index.

  `sql`

    The SQL query used to generate the index.

  `columns`

    A list of columns that are covered by the index.

  `unique`

    A boolean value indicating whether the index has a unique constraint.

  `table`

    The name of the table containing this index.

class `ColumnMetadata`(name, data_type, null, primary_key, table)
  `name`

    The name of the column.

  `data_type`

    The data type of the column

  `null`

    A boolean value indicating whether `NULL` is permitted in this column.

  `primary_key`

    A boolean value indicating whether this column is a primary key.

  `table`

    The name of the table containing this column.

class `ForeignKeyMetadata`(column, dest_table, dest_column, table)
  `column`

    The column containing the foreign key (the “source”).

  `dest_table`

    The table referenced by the foreign key.

  `dest_column`

    The column referenced by the foreign key (on `dest_table`).

  `table`

    The name of the table containing this foreign key.

## Misc

class `fn`

  A helper class that will convert arbitrary function calls to SQL function calls.

  To express functions in peewee, use the `fn` object. The way it works is anything to the right of the “dot” operator will be treated as a function. You can pass that function arbitrary parameters which can be other valid expressions.

  For example:

  Peewee expression | Equivalent SQL
  --- | ---
  `fn.Count(Tweet.id).alias('count')` | `Count(t1."id") AS count`
  `fn.Lower(fn.Substr(User.username, 1, 1))` | `Lower(Substr(t1."username", 1, 1))`
  `fn.Rand().alias('random')` | `Rand() AS random`
  `fn.Stddev(Employee.salary).alias('sdv')` | `Stddev(t1."salary") AS sdv`

  `over`([partition_by=None[, order_by=None[, window=None]]])

    Basic support for SQL window functions.

    Parameters: | partition_by (list) – List of `Node` instances to partition by. order_by (list) – List of `Node` instances to use for ordering. window (Window) – A `Window` instance to use for this aggregate.

    Examples:

        # Get the list of employees and the average salary for their dept.
        query = (Employee
                 .select(
                     Employee.name,
                     Employee.department,
                     Employee.salary,
                     fn.Avg(Employee.salary).over(
                         partition_by=[Employee.department]))
                 .order_by(Employee.name))

        # Rank employees by salary.
        query = (Employee
                 .select(
                     Employee.name,
                     Employee.salary,
                     fn.rank().over(
                         order_by=[Employee.salary])))

        # Get a list of page-views, along with avg pageviews for that day.
        query = (PageView
                 .select(
                     PageView.url,
                     PageView.timestamp,
                     fn.Count(PageView.id).over(
                         partition_by=[fn.date_trunc(
                             'day', PageView.timestamp)]))
                 .order_by(PageView.timestamp))

        # Same as above but using a window class.
        window = Window(partition_by=[fn.date_trunc('day', PageView.timestamp)])
        query = (PageView
                 .select(
                     PageView.url,
                     PageView.timestamp,
                     fn.Count(PageView.id).over(window=window))
                 .window(window)  # Need to include our Window here.
                 .order_by(PageView.timestamp))

class `SQL`(sql, *params)

  Add fragments of SQL to a peewee query. For example you might want to reference an aliased name.

  Parameters: | sql (str) – Arbitrary SQL string. params – Arbitrary query parameters.

      # Retrieve user table and "annotate" it with a count of tweets for each
      # user.
      query = (User
               .select(User, fn.Count(Tweet.id).alias('ct'))
               .join(Tweet, JOIN.LEFT_OUTER)
               .group_by(User))

      # Sort the users by number of tweets.
      query = query.order_by(SQL('ct DESC'))

class `Window`([partition_by=None[, order_by=None]])

  Create a `WINDOW` definition.

  Parameters: | partition_by (list) – List of `Node` instances to partition by. order_by (list) – List of `Node` instances to use for ordering.

  Examples:

      # Get the list of employees and the average salary for their dept.
      window = Window(partition_by=[Employee.department]).alias('dept_w')
      query = (Employee
               .select(
                   Employee.name,
                   Employee.department,
                   Employee.salary,
                   fn.Avg(Employee.salary).over(window))
               .window(window)
               .order_by(Employee.name))

class `DeferredRelation`

  Used to reference a not-yet-created model class. Stands in as a placeholder for the related model of a foreign key. Useful for circular references.

      DeferredPost = DeferredRelation()

      class User(Model):
          username = CharField()

          # `Post` is not available yet, it is declared below.
          favorite_post = ForeignKeyField(DeferredPost, null=True)

      class Post(Model):
          # `Post` comes after `User` since it refers to `User`.
          user = ForeignKeyField(User)
          title = CharField()

      DeferredPost.set_model(Post)  # Post is now available.

  `set_model`(model)

    Replace the placeholder with the correct model class.

class `Proxy`

  Proxy class useful for situations when you wish to defer the initialization of an object. For instance, you want to define your models but you do not know what database engine you will be using until runtime.

  Example:

  >     database_proxy = Proxy()  # Create a proxy for our db.

  >     class BaseModel(Model):
  >         class Meta:
  >             database = database_proxy  # Use proxy for our DB.

  >     class User(BaseModel):
  >         username = CharField()

  >     # Based on configuration, use a different database.
  >     if app.config['DEBUG']:
  >         database = SqliteDatabase('local.db')
  >     elif app.config['TESTING']:
  >         database = SqliteDatabase(':memory:')
  >     else:
  >         database = PostgresqlDatabase('mega_production_db')

  >     # Configure our proxy to use the db we specified in config.
  >     database_proxy.initialize(database)

  `initialize`(obj)

    Parameters: | obj – The object to proxy to.

    Once initialized, the attributes and methods on `obj` can be accessed directly via the `Proxy` instance.

class `Node`

  The `Node` class is the parent class for all composable parts of a query, and forms the basis of peewee’s expression API. The following classes extend `Node`:

  - `SelectQuery`, `UpdateQuery`, `InsertQuery`, `DeleteQuery`, and `RawQuery`.
  - `Field`
  - `Func` (and `fn()`)
  - `SQL`
  - `Expression`
  - `Param`
  - `Window`
  - `Clause`
  - `Entity`
  - `Check`

  Overridden operators:

  - Bitwise and- and or- (`&` and `|`): combine multiple nodes using the given conjunction.
  - `+`, `-`, `*`, `/` and `^` (add, subtract, multiply, divide and exclusive-or).
  - `==`, `!=`, `<`, `<=`, `>`, `>=`: create a binary expression using the given comparator.
  - `<<`: create an IN expression.
  - `>>`: create an IS expression.
  - `%` and `**`: LIKE and ILIKE.
  `contains`(rhs)

    Create a binary expression using case-insensitive string search.

  `startswith`(rhs)

    Create a binary expression using case-insensitive prefix search.

  `endswith`(rhs)

    Create a binary expression using case-insensitive suffix search.

  `between`(low, high)

    Create an expression that will match values between `low` and `high`.

  `regexp`(expression)

    Match based on regular expression.

  `concat`(rhs)

    Concatenate the current node with the provided `rhs`.

  `is_null`([is_null=True])

    Create an expression testing whether the `Node` is (or is not) `NULL`.

        # Find all categories whose parent column is NULL.
        root_nodes = Category.select().where(Category.parent.is_null())

        # Find all categores whose parent is NOT NULL.
        child_nodes = Category.select().where(Category.parent.is_null(False))

    To simplify things, peewee will generate the correct SQL for equality and inequality. The `is_null()` method is provided simply for readability.

        # Equivalent to the previous queries -- peewee will translate these
        # into `IS NULL` and `IS NOT NULL`:
        root_nodes = Category.select().where(Category.parent == None)
        child_nodes = Category.select().where(Category.parent != None)

  `__invert__`()

    Negate the node. This translates roughly into NOT (<node>).

  `alias`([name=None])

    Apply an alias to the given node. This translates into <node> AS <name>.

  `asc`()

    Apply ascending ordering to the given node. This translates into <node> ASC.

  `desc`()

    Apply descending ordering to the given node. This translates into <node> DESC.

  `bind_to`(model_class)

    Bind the results of an expression to a specific model type. Useful when adding expressions to a select, where the result of the expression should be placed on a particular joined instance.

  classmethod `extend`([name=None[, clone=False]])

    Decorator for adding the decorated function as a new method on `Node` and its subclasses. Useful for adding implementation-specific features to all node types.

    Parameters: | name (str) – Method name. If not provided the name of the wrapped function will be used. clone (bool) – Whether this method should return a clone. This is generally true when the method mutates the internal state of the node.

    Example:

        # Add a `cast()` method to all nodes using the '::' operator.
        PostgresqlDatabase.register_ops({'::', '::'})

        @Node.extend()
        def cast(self, as_type):
            return Expression(self, '::', SQL(as_type))

        # Let's pretend we want to find all data points whose numbers
        # are palindromes. Note that we can use the new *cast* method
        # on both fields and with the `fn` helper:
        reverse_val = fn.REVERSE(DataModel.value.cast('str')).cast('int')

        query = (DataPoint
                 .select()
                 .where(DataPoint.value == reverse_val))

    Note

    To remove an extended method, simply call `delattr` on the class the method was originally added to.